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<p><img src="https://gitee.com/wrj1006er/pic/raw/master/pic/QQ%E6%88%AA%E5%9B%BE20211207194329.jpg" alt="RUNOOB 图标"><br><img src="https://gitee.com/wrj1006er/pic/raw/master/pic/QQ%E6%88%AA%E5%9B%BE20211207194407.jpg" alt="RUNOOB 图标"></p>
<p>算力资源信息相对于IP路由信息，信息的维度更高，变化的频率更快，所以在传播的过程中会给网络带来巨大的开销。</p>
<h2 id="sketch方法直接压缩"><a href="#sketch方法直接压缩" class="headerlink" title="sketch方法直接压缩"></a>sketch方法直接压缩</h2><p>Sketch 是基于哈希的亚线性数据结构，常用于流量数据统计，通过不同的哈希函数，将有相同哈希结果的值存入相同的桶内，无需存储全部的网络流，从而减少存储开销，通过查询操作可以获得流量统计数据。<br>sketch存储的不是数据的本身，而是数据出现的频次，所以对于路由中算力资源信息的压缩传播，目前没有找到利用sketch存储的方法。</p>
<h2 id="利用传统数据压缩算法直接对数据进行压缩"><a href="#利用传统数据压缩算法直接对数据进行压缩" class="headerlink" title="利用传统数据压缩算法直接对数据进行压缩"></a>利用传统数据压缩算法直接对数据进行压缩</h2><h3 id="LZ77压缩算法"><a href="#LZ77压缩算法" class="headerlink" title="LZ77压缩算法"></a>LZ77压缩算法</h3><p>是一个简单但十分高效的数据压缩算法。其方式就是把数据中一些可以组织成短语(最长字符)的字符加入字典，然后再有相同字符出现采用标记来代替字典中的短语。</p>
<p>主要包含这几个部分，短语字典，滑动窗口，向前缓冲区。</p>
<ul>
<li>向前缓冲区：每次读取数据的时候，先把一部分数据预载入前向缓冲区。为移入滑动窗口做准备</li>
<li>一旦数据通过缓冲区，那么它将以动到滑动窗口中，编程字典的一部分。滑动窗口需要预设一个定值。</li>
<li>从字符序列S1 …. Sn, 组成n个短语。比如字符(A,B,D)，可以组合的短语为 {(A), (A,B), (A, B, D), (B), (B, D), (D)}<br>总结：压缩的过程耗时，但是解压很快。<h3 id="LZW字典压缩"><a href="#LZW字典压缩" class="headerlink" title="LZW字典压缩"></a>LZW字典压缩</h3>LZW字典压缩的精髓就是通过字典去记录一些出现频次比较高的词汇，并在存储的时候把一些词汇用特定的数去表示。在压缩的过程中动态的生成词典来记录高频词汇。<h3 id="哈夫曼编码"><a href="#哈夫曼编码" class="headerlink" title="哈夫曼编码"></a>哈夫曼编码</h3>对于出现频率较高的数据用较短的编码进行记录，对于出现频率较低的数据用较长的编码进行记录。</li>
</ul>
<p>主要就是将数据库中的数据导出后进行直接压缩(gzip,bzip,xz)</p>
<h3 id="哈夫曼编码-1"><a href="#哈夫曼编码-1" class="headerlink" title="哈夫曼编码"></a>哈夫曼编码</h3><h4 id="有损算法"><a href="#有损算法" class="headerlink" title="有损算法"></a>有损算法</h4><p>对于表格中的算力度量的信息，并不一定要求完全的精准，先通过聚类的算法，按照服务器算力进行分类（包括算力资源，运行算法，数据类型），依据不同种类的算力资源出现的频率，进行哈夫曼编码。对于表格来说，利用一些聚类算法进行分类，然后对每一类进数量的统计与编码。以K-Means为例，可以通过人为的定义加上计算欧式距离来区分，k值得选择会影响算法得精确度。</p>
<h4 id="无损算法"><a href="#无损算法" class="headerlink" title="无损算法"></a>无损算法</h4><p>对于表格中的数据进行分析来看，对于CPU核数，内存总量，磁盘总量的数据，重复性很高，对于CPU使用率，内存使用率，磁盘使用率等信息重复性并不高。只对硬件数据进行哈夫曼编码的压缩。</p>
<p>这种方法是针对单个路由节点的，也就是说每个路由节点都要发布的信息包括</p>
<ol>
<li>压缩后的表各</li>
<li>哈夫曼编码到算力信息的映射表。</li>
</ol>
<h3 id="霍夫曼编码-sketch"><a href="#霍夫曼编码-sketch" class="headerlink" title="霍夫曼编码+sketch"></a>霍夫曼编码+sketch</h3><p>前面的编码的依据是通过哈夫曼编码对每个算力路由节点中数据统计得出的。但是在实际数据传播过程中，不一定算力网络中拥有最多种类得机器类型在网络传播次数也是最多得。可以通过在路由节点上利用sketch算法对不同算力资源得频率进行统计。再进行哈夫曼编码。</p>
<h3 id="问题"><a href="#问题" class="headerlink" title="问题"></a>问题</h3><p>算力网络中由于个个算力是会实时变化的所以，对于这种预先对格式编码的方法，算力网络中会有新的算力节点的加入或者，原有节点状态的改变，可能会导致编码的改变，或者是编码映射的变化。存在的问题是在数据统计量发生变化的时候如何做到数据的同步。</p>
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href="#%E6%95%B0%E6%8D%AE%E5%8E%8B%E7%BC%A9%E6%96%B9%E6%B3%95%E8%B0%83%E7%A0%94"><span class="toc-number">1.</span> <span class="toc-text">数据压缩方法调研</span></a><ol class="toc-child"><li class="toc-item toc-level-2"><a class="toc-link" href="#sketch%E6%96%B9%E6%B3%95%E7%9B%B4%E6%8E%A5%E5%8E%8B%E7%BC%A9"><span class="toc-number">1.1.</span> <span class="toc-text">sketch方法直接压缩</span></a></li><li class="toc-item toc-level-2"><a class="toc-link" href="#%E5%88%A9%E7%94%A8%E4%BC%A0%E7%BB%9F%E6%95%B0%E6%8D%AE%E5%8E%8B%E7%BC%A9%E7%AE%97%E6%B3%95%E7%9B%B4%E6%8E%A5%E5%AF%B9%E6%95%B0%E6%8D%AE%E8%BF%9B%E8%A1%8C%E5%8E%8B%E7%BC%A9"><span class="toc-number">1.2.</span> <span class="toc-text">利用传统数据压缩算法直接对数据进行压缩</span></a><ol class="toc-child"><li class="toc-item toc-level-3"><a class="toc-link" href="#LZ77%E5%8E%8B%E7%BC%A9%E7%AE%97%E6%B3%95"><span class="toc-number">1.2.1.</span> <span class="toc-text">LZ77压缩算法</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#LZW%E5%AD%97%E5%85%B8%E5%8E%8B%E7%BC%A9"><span class="toc-number">1.2.2.</span> <span class="toc-text">LZW字典压缩</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E5%93%88%E5%A4%AB%E6%9B%BC%E7%BC%96%E7%A0%81"><span class="toc-number">1.2.3.</span> <span class="toc-text">哈夫曼编码</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E5%93%88%E5%A4%AB%E6%9B%BC%E7%BC%96%E7%A0%81-1"><span class="toc-number">1.2.4.</span> <span class="toc-text">哈夫曼编码</span></a><ol class="toc-child"><li class="toc-item toc-level-4"><a class="toc-link" href="#%E6%9C%89%E6%8D%9F%E7%AE%97%E6%B3%95"><span class="toc-number">1.2.4.1.</span> <span class="toc-text">有损算法</span></a></li><li class="toc-item toc-level-4"><a class="toc-link" href="#%E6%97%A0%E6%8D%9F%E7%AE%97%E6%B3%95"><span class="toc-number">1.2.4.2.</span> <span class="toc-text">无损算法</span></a></li></ol></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E9%9C%8D%E5%A4%AB%E6%9B%BC%E7%BC%96%E7%A0%81-sketch"><span class="toc-number">1.2.5.</span> <span class="toc-text">霍夫曼编码+sketch</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E9%97%AE%E9%A2%98"><span class="toc-number">1.2.6.</span> <span class="toc-text">问题</span></a></li></ol></li></ol></li></ol></div></div><div class="card-widget card-recent-post"><div class="item-headline"><i class="fas fa-history"></i><span>最新文章</span></div><div class="aside-list"><div class="aside-list-item"><a class="thumbnail" href="/2022/01/19/redis-cluster/" title="redis-cluster"><img src="https://cdn.jsdelivr.net/npm/butterfly-extsrc@1/img/default.jpg" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="redis-cluster"/></a><div class="content"><a class="title" href="/2022/01/19/redis-cluster/" title="redis-cluster">redis-cluster</a><time datetime="2022-01-19T13:23:30.000Z" title="发表于 2022-01-19 21:23:30">2022-01-19</time></div></div><div class="aside-list-item"><a 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